Contents

NumPy

Tomas Beuzen, September 2020

These exercises complement Chapter 5 and Chapter 6.

Exercises

1.

Import numpy under the alias np.

# Your answer here.

2.

Create the following arrays:

  1. Create an array of 5 zeros.

  2. Create an array of 10 ones.

  3. Create an array of 5 3.141s.

  4. Create an array of the integers 1 to 20.

  5. Create a 5 x 5 matrix of ones with a dtype int.

# Your answer here.

3.

Use numpy to:

  1. Create an 3D matrix of 3 x 3 x 3 full of random numbers drawn from a standard normal distribution (hint: np.random.randn())

  2. Reshape the above array into shape (27,)

# Your answer here.

4.

Create an array of 20 linearly spaced numbers between 1 and 10.

# Your answer here.

5.

Run the following code to create an array of shape 4 x 4 and then use indexing to produce the outputs shown below.

import numpy as np
a = np.arange(1, 26).reshape(5, -1)

20

# Your answer here.

array([[ 9, 10],
       [14, 15],
       [19, 20],
       [24, 25]])

# Your answer here.

array([ 6,  7,  8,  9, 10])

# Your answer here.

array([[11, 12, 13, 14, 15],
       [16, 17, 18, 19, 20]])

# Your answer here.

array([[ 8,  9],
       [13, 14]])

# Your answer here.

6.

Calculate the sum of all the numbers in a.

# Your answer here.

7.

Calculate the sum of each row in a.

# Your answer here.

8.

Extract all values of a greater than the mean of a (hint: use a boolean mask).

# Your answer here.

9.

Find the location of the minimum value in the following array b:

np.random.seed(123)
b = np.random.randn(10)
b

array([-1.0856306 ,  0.99734545,  0.2829785 , -1.50629471, -0.57860025,
        1.65143654, -2.42667924, -0.42891263,  1.26593626, -0.8667404 ])

# Your answer here.

10.

Find the location of the maximum value in the following 2D array c (hint: there are many ways to do this question, but a quick search on stackoverflow.com will typically help you find the optimum solution for a problem, for example see post):

np.random.seed(123)
c = np.random.randn(3, 2)
c

array([[-1.0856306 ,  0.99734545],
       [ 0.2829785 , -1.50629471],
       [-0.57860025,  1.65143654]])

# Your answer here.

Solutions

1.

Import numpy under the alias np.

import numpy as np

2.

Create the following arrays:

  1. Create an array of 5 zeros.

  2. Create an array of 10 ones.

  3. Create an array of 5 3.141s.

  4. Create an array of the integers 1 to 20.

  5. Create a 5 x 5 matrix of ones with a dtype int.

print(np.zeros(5))
print(np.ones(10))
print(np.full(5, 3.141))
print(np.array(range(21)))
print(np.ones((5, 5), dtype=int))

[0. 0. 0. 0. 0.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[3.141 3.141 3.141 3.141 3.141]
[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20]
[[1 1 1 1 1]
 [1 1 1 1 1]
 [1 1 1 1 1]
 [1 1 1 1 1]
 [1 1 1 1 1]]

3.

Use numpy to:

  1. Create an 3D matrix of 3 x 3 x 3 full of random numbers drawn from a standard normal distribution (hint: np.random.randn())

  2. Reshape the above array into shape (27,)

x = np.random.randn(3, 3, 3)
x

array([[[-2.42667924, -0.42891263,  1.26593626],
        [-0.8667404 , -0.67888615, -0.09470897],
        [ 1.49138963, -0.638902  , -0.44398196]],

       [[-0.43435128,  2.20593008,  2.18678609],
        [ 1.0040539 ,  0.3861864 ,  0.73736858],
        [ 1.49073203, -0.93583387,  1.17582904]],

       [[-1.25388067, -0.6377515 ,  0.9071052 ],
        [-1.4286807 , -0.14006872, -0.8617549 ],
        [-0.25561937, -2.79858911, -1.7715331 ]]])

x.reshape(-1) # or x.reshape(27)

array([-2.42667924, -0.42891263,  1.26593626, -0.8667404 , -0.67888615,
       -0.09470897,  1.49138963, -0.638902  , -0.44398196, -0.43435128,
        2.20593008,  2.18678609,  1.0040539 ,  0.3861864 ,  0.73736858,
        1.49073203, -0.93583387,  1.17582904, -1.25388067, -0.6377515 ,
        0.9071052 , -1.4286807 , -0.14006872, -0.8617549 , -0.25561937,
       -2.79858911, -1.7715331 ])

4.

Create an array of 20 linearly spaced numbers between 1 and 10.

np.linspace(1, 10, 20)

array([ 1.        ,  1.47368421,  1.94736842,  2.42105263,  2.89473684,
        3.36842105,  3.84210526,  4.31578947,  4.78947368,  5.26315789,
        5.73684211,  6.21052632,  6.68421053,  7.15789474,  7.63157895,
        8.10526316,  8.57894737,  9.05263158,  9.52631579, 10.        ])

5.

Below I’ve defined an array of shape 4 x 4. Use indexing to procude the given outputs.

a = np.arange(1, 26).reshape(5, -1)
a

array([[ 1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10],
       [11, 12, 13, 14, 15],
       [16, 17, 18, 19, 20],
       [21, 22, 23, 24, 25]])

20

a[3,4]

20

array([[ 9, 10],
       [14, 15],
       [19, 20],
       [24, 25]])

a[1:,3:]

array([[ 9, 10],
       [14, 15],
       [19, 20],
       [24, 25]])

array([ 6,  7,  8,  9, 10])

a[1,:]

array([ 6,  7,  8,  9, 10])

array([[11, 12, 13, 14, 15],
       [16, 17, 18, 19, 20]])

array([[ 8,  9],
       [13, 14]])

a[1:3,2:4]

array([[ 8,  9],
       [13, 14]])

6.

Calculate the sum of all the numbers in a.

a.sum()

325

7.

Calculate the sum of each row in a.

a.sum(axis=1)

array([ 15,  40,  65,  90, 115])

8.

Extract all values of a greater than the mean of a (hint: use a boolean mask).

a[a > a.mean()]

array([14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25])

9.

Find the location of the minimum value in the following array b:

np.random.seed(123)
b = np.random.randn(10)
b

array([-1.0856306 ,  0.99734545,  0.2829785 , -1.50629471, -0.57860025,
        1.65143654, -2.42667924, -0.42891263,  1.26593626, -0.8667404 ])

b.argmin()

6

10.

Find the location of the maximum value in the following 2D array c (hint: there are many ways to do this question, but a quick search on stackoverflow.com will typically help you find the optimum solution for a problem, for example see post):

np.random.seed(123)
c = np.random.randn(3, 2)
c

array([[-1.0856306 ,  0.99734545],
       [ 0.2829785 , -1.50629471],
       [-0.57860025,  1.65143654]])

print(f"Location of maximum: {np.unravel_index(c.argmax(), c.shape)}")
print(f"   Value of maximum: {c.max():.2f}")

Location of maximum: (2, 1)
   Value of maximum: 1.65
Style Guides, Scripts, Imports Pandas